## All dosed subjects have to be analyzed [General Sta­tis­tics]

Hi sedhosen,

» Is it also not acceptable to replace subjects even if the number of excluded subjects is more than the number of considered dropouts?

Acc. to the GL, yes.

» For example, if 7 subjects are withdrawn during the study after receiving the dose, and the number of considered dropouts are 5 subjects, is there any way we can replace 2 subjects that were withdrawn over the number of dropouts?

Two points:
1. The impact of dropouts on power is overrated by many (you are not alone). An example with a dropout-rate of 15% and two less eligible subjects than anticipated:
Does it really matter? Remember that the sample estimation is based on assumptions – it’s not an exact calculation.
   CV  n  power dosed eligible power.actual  0.20 20 0.8347    24       18       0.7912  0.25 28 0.8074    34       26       0.7761  0.30 40 0.8158    48       38       0.7953  0.35 52 0.8075    62       50       0.7917
-scrip at the end.
2. If you would dose two subjects later, it could complicate the statistical model because periods differ. Of course, you could ignore that but I have seen nasty questions from regulators.

ibrary(PowerTOST) up2even <- function(n, ns = 2) {   return(as.integer(ns * (n %/% ns + as.logical(n %% ns)))) } nadj <- function(n, do.rate, ns = 2) {   return(as.integer(up2even(n / (1 - do.rate), ns))) } design  <- "2x2x2" CV      <- seq(0.2, 0.35, 0.05) theta0  <- 0.95 # T/R-ratio target  <- 0.80 # desired power do.rate <- 0.15 # 15% if (design == "parallel") {   ns <- 2L } else {   ns <- as.integer(substr(design, 3, 3)) } df <- data.frame(CV = CV, n = NA, power = NA,                  dosed = NA, eligible = NA, power.actual = NA) for (j in 1:nrow(df)) {   tmp <- sampleN.TOST(CV = CV[j], theta0 = theta0,                       targetpower = target,                       design = design, print = FALSE)   df[j, 2:3] <- tmp[7:8]   df[j, 4]   <- nadj(df[j, 2], do.rate, ns)   df[j, 5]   <- df[j, 2] - 2   df[j, 6]   <- power.TOST(CV = CV[j], theta0 = theta0,                            design = design, n = df[j, 5]) } print(signif(df, 4), row.names = FALSE)

Dif-tor heh smusma 🖖
Helmut Schütz

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